University of Florida Research Foundation, Incorporated

Justin C Sanchez

Jose Carlos Principe

Toshikazu Nishida

John Gregory Harris

Rizwan Bashirullah

Babak Mahmoudi

Jeremiah D Mitzelfelt

Paul R Camey

John F Digiovanna

Erin E Patrick

A Brain Machine Interface (BMI) agent () is provided, that when operatively coupled to a subject during a mental task by the subject to control a prosthetic device (), monitors one or more states () of neural activity of the subject, receives feedback associated with a behavior of the prosthetic device responsive to the control, learns a functional mapping between the mental task and the behavior in view of the feedback, and applies at least one control action () to the prosthetic device in accordance with the learning to control the prosthetic device for a targeted behavior.

System and method for brain machine interface (BMI) control using reinforcement learning

In one embodiment, a neural interface system includes an implantable neural probe having a flexible substrate, electrodes that extend from the substrate that are adapted to contact neural tissue of the brain, a signal processing circuit configured to process neural signals collected with the electrodes, and a wireless transmission circuit configured to wirelessly transmit the processed neural signals, and a backend computing device configured to wirelessly receive the processed neural signals, to process the received signals to reconstruct the collected neural signals, and to analyze the collected neural signals.

Neural interface systems and methods

The invention provides a micro-control neuroprosthetic device and methods for predicting and controlling epileptic neuronal activity. The device includes a detection system that detects and collects electrophysiological information comprising action potentials from single neurons and ensembles of neurons in a neural structure such as an epileptogenic region of the brain in a subject. An analysis system included in the neuroprosthetic device evaluates the electrophysiological information and performs a real-time extraction of neuron firing features from which the system determines when stimulus intervention is required. The neuroprosthetic device further comprises a stimulation intervention system that provides stimulus output signals having a desired stimulation frequency and stimulation intensity directly to the neural structure in which abnormal neuronal activity is detected. The analysis system further analyzes collected electrophysiological information during or following stimulus intervention to assess the effects of the stimulation intervention and to provide outputs to maintain or modify the stimulation intervention.

Closed-loop micro-control system for predicting and preventing epileptic seizures

A method of fabricating a MEMS flexible substrate neural probe is provided. The method can include applying an insulation layer on a substrate, and depositing a plurality of metal traces on the insulation layer and electroplating each of the plurality of traces. The method also can include encapsulating the insulation layer and metal traces deposited thereon with an insulation layer. Additionally the method can include etching the insulation layer to form a plurality bond pad sites and probes to form a flexible ribbon cable having a plurality of bond pad sites disposed on a surface of the flexible cable and a plurality of neural probes extending from the flexible cable. The method further can include separating the substrate from the insulation layer and depositing insulation on each of the neural probes, each probe comprising insulated portion and exposed metallic tip. Moreover, the method can include cutting each of the exposed metallic tips, and plating each of the exposed metallic tips and each of the plurality of bond pad sites.

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Gainesville, FL, United States of America

Justin C Sanchez